Software Engineer - Data Scientist

Johnson Controls; Pune District; Hybrid; INR 1,200,000 - 2,100,000; Full time

Benefits offered by this job

Hybrid work model

Certification sponsorship

Learning budget

Mentorship from senior data scientists

Job summary

Johnson Controls seeks a Junior Data Scientist to tackle business problems with data, ML, and AI. You will build scalable models, deploy AI solutions on Azure, and extract actionable insights from large datasets.

The role requires strong Python skills, ML frameworks, and familiarity with Azure services. You will collaborate with data engineers to ensure data quality and governance across Azure data platforms in a hybrid work environment.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
• 4–7 years of professional or project-based experience in data science or ML.
• Strong proficiency in Python (pandas, NumPy, scikit-learn).
• Hands-on experience with Azure services (Azure ML, Databricks, Data Factory, Synapse).
• Understanding of supervised/unsupervised learning, model evaluation, and hyperparameter tuning.
• Experience with deep learning frameworks (TensorFlow or PyTorch).
• SQL for querying relational databases and analytics.
• Familiarity with MLOps: MLflow, CI/CD for ML.

Responsibilities

• Design, build, and evaluate ML models for classification, regression, forecasting, and NLP use cases.
• Develop and maintain data pipelines using Python and Azure Data Factory / Azure Databricks.
• Deploy ML models on Azure ML with endpoints and pipelines.
• Collaborate with data engineers to ensure data quality, governance, and accessibility in Azure Data Lake and Synapse.
• Apply AI/GenAI capabilities (Azure OpenAI, Cognitive Services) to build intelligent apps.
• Monitor model performance, detect drift, and retrain as needed.
• Translate business requirements into ML problem statements and communicate findings to stakeholders.
• Participate in code reviews, documentation, and ML Ops best practices.

Skills

Python

SQL

Machine Learning

Deep Learning

MLOps

Power BI

Azure ML

Azure Databricks

Azure Data Factory

GitHub Actions

Education

Bachelor's or Master's in CS/DS/Engineering/Math

Tools

Azure Databricks

Azure Data Factory

Azure Synapse

Azure Blob Storage

TensorFlow/PyTorch

MLflow

Docker/Kubernetes

Job description

Junior Data Scientist

Full-Time - 4-5 Years Experience

Department Data Science & AI

Location Hybrid / On-site

Experience 4-5 Years

Employment Type Full-Time

Notice Period Immediate Joiners Preferred

About the Role

We are hiring a Junior Data Scientist who is passionate about solving complex business problems using data, machine learning, and AI. The ideal candidate has a strong foundation in Python, hands‑on experience with ML frameworks, and exposure to Microsoft Azure cloud services. You will work on developing scalable ML models, deploying AI solutions, and deriving actionable insights from large datasets.

Key Responsibilities

• Design, build, and evaluate machine learning models for classification, regression, forecasting, and NLP use cases.


• Develop and maintain data pipelines using Python and Azure Data Factory / Azure Databricks for ETL and feature engineering.


• Deploy ML models on Azure Machine Learning (Azure ML) using endpoints, pipelines, and MLflow tracking.


• Collaborate with data engineers to ensure data quality, availability, and governance across Azure Data Lake and Azure Synapse Analytics.


• Apply AI/GenAI capabilities (Azure OpenAI, Cognitive Services) to build intelligent applications and automation workflows.


• Monitor model performance in production, identify drift, and implement retraining strategies.


• Translate business requirements into data science problem statements and communicate findings to stakeholders.


• Participate in code reviews, documentation, and adherence to ML Ops best practices.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.


• 4 to 7 years of professional or project-based experience in data science or machine learning.


• Strong proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM).


• Hands‑on experience with Microsoft Azure services: Azure ML, Azure Databricks, Azure Data Factory, Azure Blob Storage, or Azure Synapse.


• Understanding of supervised and unsupervised learning algorithms, model evaluation, and hyperparameter tuning.


• Experience with deep learning frameworks: TensorFlow or PyTorch (at least one required).


• Solid SQL skills for querying relational databases and analytical processing.


• Familiarity with MLOps practices: experiment tracking (MLflow), model versioning, and CI/CD for ML.


• Experience with data visualization tools (Power BI, Matplotlib, Seaborn, or Plotly).

Good to Have

• Microsoft Azure certifications: AZ-900, AI-900, DP-100 (Azure Data Scientist Associate) preferred.


• Experience with NLP libraries (Hugging Face, spaCy, NLTK) and LLM integrations (Azure OpenAI, LangChain).


• Knowledge of containerization and deployment: Docker, Kubernetes, or Azure Container Instances.


• Familiarity with big data tools: Apache Spark (PySpark) via Azure Databricks.


• Exposure to Generative AI, RAG (Retrieval-Augmented Generation), or Prompt Engineering.


• Version control using Git and experience with Agile/Scrum development methodology.

Technical Stack

• Languages: Python, SQL


• ML/AI Frameworks: scikit-learn, XGBoost, TensorFlow, PyTorch, Hugging Face


• Cloud Platform: Microsoft Azure (Azure ML, Databricks, Data Factory, Synapse, OpenAI)


• MLOps Tools: MLflow, Azure DevOps, GitHub Actions


• Data & BI Tools: Power BI, Pandas, PySpark, Jupyter


• Storage & DB: Azure Blob Storage, Azure Data Lake, SQL Server, Cosmos DB

What We Offer

• Competitive salary and performance-based incentives.


• Azure certification sponsorship and continuous learning budget.


• Mentorship from senior data scientists and ML architects.


• Exposure to cutting‑edge AI/ML projects across domains.


• Flexible hybrid working model and collaborative culture.